Future Mining: Learning for Safety and Security
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915794182471680 |
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| author | Rahman, Md Sazedur Jewel, Mizanur Rahman Madria, Sanjay |
| author_facet | Rahman, Md Sazedur Jewel, Mizanur Rahman Madria, Sanjay |
| contents | Mining is rapidly evolving into an AI driven cyber physical ecosystem where safety and operational reliability depend on robust perception, trustworthy distributed intelligence, and continuous monitoring of miners and equipment. However, real world mining environments impose severe constraints, including poor illumination, GPS denied conditions, irregular underground topologies and intermittent connectivity. These factors degrade perception accuracy, disrupt situational awareness and weaken distributed learning systems. At the same time, emerging cyber physical threats such as backdoor triggers, sensor spoofing, label flipping attacks, and poisoned model updates further jeopardize operational safety as mines adopt autonomous vehicles, humanoid assistance, and federated learning for collaborative intelligence. Energy constrained sensors also experience uneven battery depletion, creating blind spots in safety coverage and disrupting hazard detection pipelines. This paper presents a vision for a Unified Smart Safety and Security Architecture that integrates multimodal perception, secure federated learning, reinforcement learning, DTN enabled communication, and energy aware sensing into a cohesive safety framework. We introduce five core modules: Miner Finder, Multimodal Situational Awareness, Backdoor Attack Monitor, TrustFed LFD, and IoT driven Equipment Health Monitoring. These modules collectively address miner localization, hazard understanding, federated robustness, and predictive maintenance. Together, they form an end to end framework capable of guiding miners through obstructed pathways, identifying compromised models or sensors, and ensuring mission critical equipment reliability. This work outlines a comprehensive research vision for building a resilient and trustworthy intelligent mining system capable of maintaining operational continuity under adversarial conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11472 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Future Mining: Learning for Safety and Security Rahman, Md Sazedur Jewel, Mizanur Rahman Madria, Sanjay Cryptography and Security Distributed, Parallel, and Cluster Computing Mining is rapidly evolving into an AI driven cyber physical ecosystem where safety and operational reliability depend on robust perception, trustworthy distributed intelligence, and continuous monitoring of miners and equipment. However, real world mining environments impose severe constraints, including poor illumination, GPS denied conditions, irregular underground topologies and intermittent connectivity. These factors degrade perception accuracy, disrupt situational awareness and weaken distributed learning systems. At the same time, emerging cyber physical threats such as backdoor triggers, sensor spoofing, label flipping attacks, and poisoned model updates further jeopardize operational safety as mines adopt autonomous vehicles, humanoid assistance, and federated learning for collaborative intelligence. Energy constrained sensors also experience uneven battery depletion, creating blind spots in safety coverage and disrupting hazard detection pipelines. This paper presents a vision for a Unified Smart Safety and Security Architecture that integrates multimodal perception, secure federated learning, reinforcement learning, DTN enabled communication, and energy aware sensing into a cohesive safety framework. We introduce five core modules: Miner Finder, Multimodal Situational Awareness, Backdoor Attack Monitor, TrustFed LFD, and IoT driven Equipment Health Monitoring. These modules collectively address miner localization, hazard understanding, federated robustness, and predictive maintenance. Together, they form an end to end framework capable of guiding miners through obstructed pathways, identifying compromised models or sensors, and ensuring mission critical equipment reliability. This work outlines a comprehensive research vision for building a resilient and trustworthy intelligent mining system capable of maintaining operational continuity under adversarial conditions. |
| title | Future Mining: Learning for Safety and Security |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2602.11472 |